Search

The growing interest in low-impact mobility technologies has elevated the significance of fuel cell electric vehicles (FCEVs) in the automotive sector. Given the complexity of the resulting powertrain, the need for an effective energy management strategy (EMS) becomes essential to optimize efficiency and energy consumption in vehicles with diverse energy sources. Model-based control is the main approach to address the EMS in electrified vehicles. In particular, fuel cell power is commonly represented through a 1D look-up table using the current demand as input to simplify the implementation in a vehicle control unit. Uncertainties that may be implemented in maps due to simplifying hypotheses, dynamics, ageing, etc., can be propagated to powertrain control, motivating the adoption of adaptive look-up tables for FC modelling. In this study, an extended Kalman filter (EKF) is proposed to adapt the look-up table to actual FC behaviour by measuring its power and gradually correcting calibration errors, drift, and ageing. Subsequently, a standard equivalent consumption minimization strategy (ECMS) is employed to control the FCEV. The fuel cell model is calibrated with experimental data from an FCEV. The results demonstrate that the adaptive strategy outperforms the base calibration. Following an extensive simulation campaign, an improvement of 1.1% in fuel consumption was observed. Remarkably, after just one hour of operation, there was a notable 85% reduction in fuel cell power estimation error, even when the EMS was initially fed a biased look-up table. [ABSTRACT FROM AUTHOR]

Energy management in electrified vehicles is critical and directly impacts the global operating efficiency, durability, driveability, and safety of the vehicle powertrain. Given the multitude of components of these powertrains, the complexity of the proper control is significantly higher than the conventional internal combustion engine vehicle (ICEV). Hence, several control algorithms and numerical methods have been developed and implemented in order to optimize the operation of the hybrid powertrain while complying with the required boundary conditions. In this work, a model-based method is used for predicting the impacts of a set of possible control actions, choosing the one minimizing the associated costs. In particular, the energy management technique used in the present study is the equivalent consumption minimization strategy (ECMS). The novelty of this work consists of taking into account the thermal state of the ICE for optimization. This feature was implemented by means of an extensive experimental campaign at different coolant temperatures of the ICE to calibrate the additional fuel consumption due to operating the engine outside of its optimum temperature. The results showed significant gains in both WLTC and RDE cycles. [ABSTRACT FROM AUTHOR]